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The Hartford
The Hartford
H1B Visa SponsorB2CFinanceInsurance
The Hartford is an insurance and financial services company serving individuals, families, and small to midsize businesses. Founded in 1810, it provides auto, home, and business insurance alongside employee benefits and other insurance solutions. The company is also known for its AARP-endorsed auto and home insurance programs, which provide participating members with access to exclusive benefits and discounts.
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Senior AI and Machine Learning Engineer — The Hartford, Charlotte Hybrid

Lead predictive modeling and generative AI engineering for The Hartford’s insurance business. Build governed, production-ready solutions supporting pricing, underwriting, employee benefits, and related policy workflows.

USUnited StatesCTConnecticutILIllinoisNCNorth CarolinaOHOhio
$117,200 – $175,800 / year
HybridFull-timeSeniorEnglish RequiredMachine Learning Engineer
AirflowAWSCloudGoogle Cloud PlatformPythonSDLCSQL

Description

  • Lead engineering delivery for Employee Benefits predictive models, covering pricing and underwriting models, scoring pipelines, model updates, monitoring, data validation, and production support.
  • Build, deploy, and maintain AI/ML components and data pipelines for pricing, underwriting, sales, service, renewal, and policy lifecycle processes.
  • Turn approved solution designs and architectural patterns into dependable, tested production code and workflows.
  • Support generative and agentic AI initiatives through prompt orchestration, retrieval-augmented generation, evaluation, guardrails, and ecosystem integrations.
  • Develop and operate batch and near-real-time pipelines for training, feature creation, inference, post-processing, business-rule integration, and downstream delivery.
  • Deploy and maintain production AI services, jobs, APIs, and workflows across AWS and GCP using established CI/CD, testing, observability, security, and operational practices.
  • Ensure implementation quality through code reviews, unit and integration testing, documentation, runbooks, readiness assessments, and incident-response support.
  • Coach and mentor junior engineers.
  • Work with Data Scientists, Data Engineers, Asset Owners, Underwriting, and Pricing teams to clarify requirements, validate results, resolve data issues, and align solutions with business processes.
  • Maintain governance documentation for models and pipelines, including lineage, inputs and outputs, monitoring measures, validation evidence, operational controls, and handoff materials.
  • Assess risks, bottlenecks, and operational shortcomings, then recommend practical improvements.

Requirements

  • Bachelor’s degree in a related field or at least six years of equivalent experience in software engineering, data engineering, ML/DevOps engineering, applied AI engineering, or a closely related technical discipline.
  • Advanced hands-on experience with Python, SQL, software development lifecycle practices, Git-based development, automated testing, and production code delivery.
  • Experience deploying and operating data, AI, or ML workloads in AWS and GCP, including cloud storage, managed compute, orchestration, IAM-aware access, logging, and monitoring.
  • Practical knowledge of ML engineering, including feature pipelines, training workflows, batch scoring, inference services, model monitoring, drift detection, validation, retraining, and production support.
  • Ability to work within established architecture, enterprise security requirements, data governance standards, coding conventions, and operational controls.
  • Ability to lead implementation efforts, mentor junior engineers, explain tradeoffs, and manage multiple model and pipeline deliverables with limited daily supervision.
  • Authorization to work in the United States without company sponsorship.
  • A master’s degree in computer science, engineering, information technology, MIS, data science, or a related field is preferred.
  • Experience with insurance, employee benefits, pricing, underwriting, risk selection, sales enablement, or policy lifecycle analytics.
  • Experience supporting predictive model portfolios that require scheduled refreshes, performance tracking, business validation, and governed production deployment.
  • Experience implementing generative or agentic AI patterns, including RAG, prompt evaluation, LLM application integration, AI safety controls, human-in-the-loop processes, and output validation.
  • Experience with orchestration and workflow platforms such as Airflow, Cloud Composer, Step Functions, Vertex AI Pipelines, or comparable enterprise tools.
  • Experience with CI/CD, containers, APIs, infrastructure-as-code concepts, observability, and production incident management.

Benefits

  • Eligibility for short-term or annual bonus programs.
  • Access to long-term incentive opportunities.
  • On-the-spot recognition awards.
  • Hybrid work arrangement.

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